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An Overview of Parameter and Data Strategies for K-Nearest Neighbours Based Short-Term Traffic Prediction
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.ORCID iD: 0000-0001-5824-425X
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
2017 (English)In: ACM International Conference Proceeding Series Volume Part F133326, Association for Computing Machinery (ACM), 2017, p. 68-74Conference paper, Published paper (Refereed)
Abstract [en]

Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flowaware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.

Place, publisher, year, edition, pages
Association for Computing Machinery (ACM), 2017. p. 68-74
Keywords [en]
Short-Term Traffic Prediction, k-Nearest Neighbours Regression, Parameter and Data Strategies
National Category
Computer Sciences Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:bth-15210DOI: 10.1145/3157737.3157749ISBN: 9781450353762 OAI: oai:DiVA.org:bth-15210DiVA, id: diva2:1145423
Conference
International Conference on Intelligent Traffic and Transportation (ICITT), Zurich
Available from: 2017-09-28 Created: 2017-09-28 Last updated: 2018-02-09Bibliographically approved

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Sun, BinWei, ChengPrashant, GoswamiGuohua, Bai
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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More styles
Language
  • de-DE
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More languages
Output format
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